By taking advantage of the attention mechanism, TS-CAM [1]} is capable of capturing long-range dependency among different parts of an image. As a result, it can efficiently separate background and foreground objects. In other words, it first divides the images into a set of patches for capturing long-range dependency information and records its effects in class token. The attention of class token is then fused with the semantic aware map to produce the final attention/activation map. The flow diagram of TS-CAM is depicted in Fig.REF . Lastly, a detailed visualization of the internal representation of the token TS-CAM class is presented in Fig.REF . It shows that the average of all maps could potentially include noise and background regions in the final prediction.
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